In the context of automated lane changing, the role of communication is less understood. This limits the efficacy of existing models, which attempt to balance personal and societal objectives using locally observable data, e.g. the state of neighboring vehicles.
This limitation is particularly evident in congested settings, in which more egoistic lane change models often contribute to degradation in throughput (an issue previously noted for similar longitudinal controllers [1]}), while more altruistic models produce imbalances in performance between humans and CAVs (we illustrate this in Section REF ).
Knowledge of the source of congestion, we hypothesize, may help alleviate these concerns.
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